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Vision Statement: Park University will be a renowned international leader in providing innovative educational opportunities for learners within the global society.
Course  MA 120 Basic Concepts of Statistics 
Semester  S1B 2011 BLB 
Faculty  Ordaz, RuthAnn W. 
Title  Senior Instructor of Mathematics 
Degrees/Certificates  B.A. in Mathematics; S.D.S.U. M.A. in Ed. Psych.; University of Minnesota 36 grad. hrs. beyond Masters' degree 
Office Location  Fort Bliss campus 
Office Hours  Before and after class as needed 
Daytime Phone  9154439287 
EMail  ruthann.ordaz@park.edu 
 WingerDing@aol.com 
Semester Dates  Jan. 10  March 6, 2011 
Class Days  TTh 
Class Time  2:00  4:30 PM 
Credit Hours  3 
Textbook:
TEXT:
Required Text: Elementary Statistics, 11th Ed.
Author: Mario F. Triola
Publisher: AddisonWesley
ISBN10: 0321500245
ISBN13: 9780321500243
Order text at: http://direct.mbsbooks.com/park.htm
Pub. Date: 12/28/2008


Textbooks can be purchased through the MBS bookstore
Additional Resources:
A basic scientific calculator is required, and one with statistical capabilities such as a TI30X is highly recommended. The most userfriendly and economical calculator for statistics is the slate blue TI30X MultiView.
A graphing calculator such as a TI83+ may also be used.
McAfee Memorial Library  Online information, links, electronic databases and the Online catalog. Contact the library for further assistance via email or at 8002704347.
Career Counseling  The Career Development Center (CDC) provides services for all stages of career development. The mission of the CDC is to provide the career planning tools to ensure a lifetime of career success.
Park Helpdesk  If you have forgotten your OPEN ID or Password, or need assistance with your PirateMail account, please email helpdesk@park.edu or call 8009273024
Resources for Current Students  A great place to look for all kinds of information http://www.park.edu/Current/.
http://www.mhhe.com/math/stat/bluman4e/
http://www.davidmlane.com/hyperstat/index.html
http://statsoft.com/textbook/stathome.html
http://psych.rice.edu/online_stat/index.html
Course Description:
A development of certain basic concepts in probability and statistics that is pertinent to most disciplines. Topics include: probability models, parameters, statistics and sampling procedures, hypothesis testing, correlation and regression. 3:0:3Educational Philosophy:
In order to understand statistics, the student must be engaged in the process of analyzing data. Teaching techniques will include lectures with detailed examples, illustrated worksheets, collaborative learning activities, and work with data sets using computer technology.
Learning Outcomes:
Core Learning Outcomes
 Compute descriptive statistics for raw data as well as grouped data.
 Determine appropriate features of a frequency distribution.
 Apply Chebyshev's Theorem.
 Distinguish between and provide relevant descriptions of a sample and a population.
 Apply the rules of combinatorics.
 Differentiate between classical and frequency approaches to probability.
 Apply settheoretic ideas to events.
 Apply basic rules of probability.
 Apply the concepts of specific discrete random variables and probability distributions.
 Compute probabilities of a normal distribution.
 Compute confidence intervals of means and percentages.
 Perform hypothesis tests involving one population.
 Compute regression and correlation of Bivariate data.
Instructor Learning Outcomes
 1. Explain why the median is used as the average for such data as home prices, and why the mean is used in other situations.
 2. Describe the significance of percentile in a context such as SAT or medical data.
 3. Determine and evaluate sample error as encountered in surveys.
Core Assessment:
Description of MA 120 Core Assessment
One problem with multiple parts for each numbered item, except for item #3, which contains four separate problems.
1. Compute the mean, median, mode, and standard deviation for a sample of 8 to 12 data.
2. Compute the mean and standard deviation of a grouped frequency distribution with 4 classes.
3. Compute the probability of four problems from among these kinds or combinations there of:
a. the probability of an event based upon a twodimensional table;
b. the probability of an event that involves using the addition rule;
c. the probability of an event that involves conditional probability;
d. the probability of an event that involves the use of independence of events;
e. the probability of an event based upon permutations and/or combinations;
f. the probability of an event using the multiplication rule; or
g. the probability of an event found by finding the probability of the complementary event.
4. Compute probabilities associated with a binomial random variable associated with a practical situation.
5. Compute probabilities associated with either a standard normal probability distribution or with a nonstandard normal probability distribution.
6. Compute and interpret a confidence interval for a mean and/ or for a proportion.
Link to Class Rubric
Class Assessment:
Midterm Exam: 25%
Final Exam: 30%
Classwork: 20%
Homework: 15%
Technology Project: 10%
TOTAL: 100%
The midterm exam is closed book. The final exam is mandatory and comprehensive.
Textbook, notes, and a calculator are allowed on the final.
Grading:
DATES TOPICS SECTIONS TEST/ASIGNMENTS
T Jan. 11 Introduction to Statistics Chapter 1
Th Jan. 13 Freq. Distrib. & Graphs 22, 23, 24
T Jan. 18 Measures of Center 32
Th Jan. 20 Variation 33
T Jan. 25 IQR, zscore, Empirical Rule 34
Th Jan. 27 EDA; Probability 35, 41 to 43
T Feb. 1 Prob. & Review 44
Th Feb. 3 Midterm Exam Ch. 1  4a
T Feb. 8 Probability, cont. 45 to 47
Th Feb. 10 Probability Distributions 51 to 54
T Feb. 15 Normal Distribution 61 to 63
Th Feb. 17 Sampling Distribution; CLT 64, 65
T Feb. 22 Confidence Intervals 72 to 74
Th Feb. 24 Hypothesis tests Ch. 8
T March 1 Review
Th March 3 Comprehensive Final Exam
A 90  100%
B 80  89%
C 70  79%
D 60  69%
F 0  59%
(0.5+ roundup rule applies.)
Late Submission of Course Materials:
Work turned in late will be accepted at 20% off per week.
Classroom Rules of Conduct:
Attendance and participation in group activities are necessary to be successful in this class.
It is expected that the student will demonstrate respect for the instructor and for one's peers.
Please turn off cell phones during exams and leave on "vibrate" if necessary during class sessions.
A laptop computer may be used for notetaking, but not for testtaking.
Course Topic/Dates/Assignments:
This course provides an introduction to the world of statistical analysis. Each week we'll focus on different aspects of the general topic.
In Unit 1 we'll learn what the topic of statistics entails. We'll discuss some ways to collect the needed data for a statistical study. By the end of the unit we'll have a view of how the two distinct divisions of statistics, descriptive and inferential, are related.
In Unit 2 we'll discover how to convert pure data into corrupted data, also referred to as ungrouped data into grouped data. Then we will examine some of the many ways data can be visually displayed graphically.
In Unit 3 we will examine ways to describe data by looking at its central tendency, its variation from its center, and how to determine the location of an element within a data set. A method of finding the proportions of variation a data set possesses will also be covered.
In Unit 4 we'll explore the basic concepts of probabilities, the branch of mathematics that allows us to take a sample and make predictions about the population from which it was derived. We'll strive to gain a fundamental understanding of probability through its addition, multiplication and counting rules.
In Unit 5 we combine the probability concepts and the statistical concepts we previously learned to construct discrete probability distributions. Then we'll learn how to find statistics of the distribution. The unit ends with a discussion on a specific discrete probability distribution called the binomial distribution.
In Unit 6 the discussion changes from discrete distributions to continuous random variable distributions. We begin looking at the Normal distribution and then quickly move on the the Standard Normal distribution. We conclude the unit by learinng how the Central Limit Theorem can be applied to sample data sets.
In Unit 7 we move into inferential statistics. We learn how to use a sample mean to estimate the population mean, and how we can confidently report its value within a specific interval.
In Unit 8 we will examine the basics of hypothesis testing by using onesample procedures for the hypothesis test of the population mean. In addition we will conclude our examination of topics in statistics by discussing the purpose of regression and correlation analysis. First, we'll examine some introductory terms, then focus on simple linear regression analysis and simple linear correlation analysis. During this final week of the course you will also complete the proctored Final Exam and the Course Evaluation.
Academic Honesty:
Academic integrity is the foundation of the academic community. Because each student has the primary responsibility for being academically honest, students are advised to read and understand all sections of this policy relating to standards of conduct and academic life. Park University students and faculty members are encouraged to take advantage of the University resources available for learning about academic honesty (www.park.edu/current or http://www.park.edu/faculty/).from Park University 20102011 Undergraduate Catalog Page 92
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Academic dishonesty includes committing or the attempt to commit cheating, plagiarism, falsifying academic records, and other acts intentionally designed to provide unfair advantage to the students.
Cheating includes, but is not limited to, intentionally giving or receiving unauthorized aid or notes on examinations, papers, laboratory reports, exercises, projects, or class assignments which are intended to be individually completed. Cheating also includes the unauthorized copying of tests or any other deceit or fraud related to the student's academic conduct.
Falsifying academic records includes, but is not limited to, altering grades or other academic records.
Other acts that constitute academic dishonesty include:
Stealing, manipulating, or interfering with an academic work of another student or faculty member.
Collusion with other students on work to be completed by one student.
Lying to or deceiving a faculty member.
Plagiarism:
Plagiarism involves the use of quotations without quotation marks, the use of quotations without indication of the source, the use of another's idea without acknowledging the source, the submission of a paper, laboratory report, project, or class assignment (any portion of such) prepared by another person, or incorrect paraphrasing. from Park University 20102011 Undergraduate Catalog Page 9293
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ALL GRADED WORK FOR THIS COURSE MUST BE YOUR OWN. EVERY QUIZ INSTRUCTION PAGE STATES THAT YOU ARE NOT TO RECEIVE OUTSIDE ASSISTANCE FROM ANYONE OTHER THAN YOUR INSTRUCTOR. To further clarify; classmates, spouses, coworkers, tutors, clergy, librarians, friends, and relatives, are included as OUTSIDE ASSISTANCE. PLEASE DO NOT VIOLATE THIS RULE.
Attendance Policy:
Instructors are required to maintain attendance records and to report absences via the online attendance reporting system.
 The instructor may excuse absences for valid reasons, but missed work must be made up within the semester/term of enrollment.
 Work missed through unexcused absences must also be made up within the semester/term of enrollment, but unexcused absences may carry further penalties.
 In the event of two consecutive weeks of unexcused absences in a semester/term of enrollment, the student will be administratively withdrawn, resulting in a grade of "F".
 A "Contract for Incomplete" will not be issued to a student who has unexcused or excessive absences recorded for a course.
 Students receiving Military Tuition Assistance or Veterans Administration educational benefits must not exceed three unexcused absences in the semester/term of enrollment. Excessive absences will be reported to the appropriate agency and may result in a monetary penalty to the student.
 Report of a "F" grade (attendance or academic) resulting from excessive absence for those students who are receiving financial assistance from agencies not mentioned in item 5 above will be reported to the appropriate agency.
Park University 20102011 Undergraduate Catalog Page 9596
Disability Guidelines:
Park University is committed to meeting the needs of all students that meet the criteria for special assistance. These guidelines are designed to supply directions to students concerning the information necessary to accomplish this goal. It is Park University's policy to comply fully with federal and state law, including Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Act of 1990, regarding students with disabilities. In the case of any inconsistency between these guidelines and federal and/or state law, the provisions of the law will apply. Additional information concerning Park University's policies and procedures related to disability can be found on the Park University web page: http://www.park.edu/disability .
Bibliography:
Notes may be downloaded from eCompanion under DocSharing.
Rubric
Competency  Exceeds Expectation (3)  Meets Expectation (2)  Does Not Meet Expectation (1)  No Evidence (0) 
Evaluation Outcomes 10  Can perform and interpret a hypothesis test with 100% accuracy.  Can perform and interpret a hypothesis test with at least 80% accuracy.  Can perform and interpret a hypothesis test with less than 80% accuracy.  Makes no attempt to perform a test of hypothesis. 

Synthesis Outcomes 10  Can compute and interpret a confidence interval for a sample mean for small and large samples, and for a proportion with 100% accuracy.  Can compute and interpret a confidence interval for a sample mean for small and large samples, and for a proportion with at least 80% accuracy.  Can compute and interpret a confidence interval for a sample mean for small and large samples, and for a proportion with less than 80% accuracy.  Makes no attempt to compute or interpret a confidence interval. 

Analysis Outcomes 10  Can apply the normal distribution, Central limit theorem, and binomial distribution to practical problems with 100% accuracy.  Can apply the normal distribution, Central limit theorem, and binomial distribution to practical problems with at least 80% accuracy.  Can apply the normal distribution, Central limit theorem, and binomial distribution to practical problems with less than 80% accuracy.  Makes no attempt to apply the normal distribution, Central Limit Theorem, or binomial distribution. 

Terminology Outcomes 4,5,7  Can explain event, simple event, mutually exclusive events, independent events, discrete random variable, continuous random variable, sample, and population with 100% accuracy.  Can explain event, simple event, mutually exclusive events, independent events, discrete random variable, continuous random variable, sample, and population with at least 80% accuracy.  Can explain event, simple event, mutually exclusive events, independent events, discrete random variable, continuous random variable, sample, and population with less than 80% accuracy.  Makes no attempt to explain any of the terms listed. 

Concepts Outcomes 1,6  Can explain mean, median, mode, standard deviation, simple probability, and measures of location with 100% accuracy.  Can explain mean, median, mode, standard deviation, simple probability, and measures of location with at least 80% accuracy.  Can explain mean, median, mode, standard deviation, simple probability, and measures of location with less than 80% accuracy.  Makes no attempt to define any concept. 

Application Outcomes 1,2,3,8,9  Compute probabilities using addition multiplication, and complement rules and conditional probabilities. Compute statistical quantities for raw and grouped data. Compute probabilities using combinatorics, discrete random variables, and continuous random variables. All must be done with 100% accuracy.  Compute probabilities using addition multiplication, and complement rules and conditional probabilities. Compute statistical quantities for raw and grouped data. Compute probabilities using combinatorics, discrete random variables, and continuous random variables. All must be done with at least 80% accuracy.  Compute probabilities using addition multiplication, and complement rules and conditional probabilities. Compute statistical quantities for raw and grouped data. Compute probabilities using combinatorics, discrete random variables, and continuous random variables. All are done with less than 80% accuracy.  Makes no attempt to compute any of the probabilities or statistics listed. 

Whole Artifact Outcomes 7,8  Can apply the concepts of probability and statistics to realworld problems in other disciplines with 100 % accuracy.  Can apply the concepts of probability and statistics to realworld problems in other disciplines with at least 80 % accuracy.  Can apply the concepts of probability and statistics to realworld problems in other disciplines with less than 80% accuracy.  Makes no attempt to apply the concepts to realworld problems. 

Components Outcomes 1  Can use a calculator or other computing device to compute statistics with 100% accuracy.  Can use a calculator or other computing device to compute statistics with at least 80% accuracy.  Can use a calculator or other computing device to compute statistics with less 80% accuracy.  Makes no attempt to use any computing device to compute statistics. 
Copyright:
This material is protected by copyright and can not be reused without author permission.Last Updated:12/14/2010 1:54:28 AM